priorMDM / priorMDM

The official implementation of the paper "Human Motion Diffusion as a Generative Prior"

PriorMDM: Human Motion Diffusion as a Generative Prior

The official PyTorch implementation of the paper "Human Motion Diffusion as a Generative Prior"(ArXiv).

Please visit our webpage for more details.

Bibtex

If you find this code useful in your research, please cite:

@article{shafir2023human,
  title={Human motion diffusion as a generative prior},
  author={Shafir, Yonatan and Tevet, Guy and Kapon, Roy and Bermano, Amit H},
  journal={arXiv preprint arXiv:2303.01418},
  year={2023}
}

Release status

	Training	Generation	Evaluation
DoubleTake (long motion)	✅	✅	✅
ComMDM (two-person)	✅	✅	✅
Fine-tuned motion control	✅	✅	✅

News

📢 29/Apr/2023 - Evaluation release of the long-motions scripts, including both datasets (BABEL & HumanML3D) - please check the updated readme.

📢 25/Apr/2023 - Full release of the fine-tuned motion control scripts.

📢 14/Apr/2023 - First release - DoubleTake/ComMDM - Training and generation with pre-trained models is available.

Getting started

This code was tested on Ubuntu 18.04.5 LTS and requires:

• Python 3.8
• conda3 or miniconda3
• CUDA capable GPU (one is enough)

1. Setup environment

Install ffmpeg (if not already installed):

sudo apt update
sudo apt install ffmpeg

For windows use this instead.

Setup conda env:

conda env create -f environment.yml
conda activate PriorMDM
python -m spacy download en_core_web_sm
pip install git+https://github.com/openai/CLIP.git
pip install git+https://github.com/GuyTevet/smplx.git

2. Get MDM dependencies

PriorMDM share most of its dependencies with the original MDM. If you already have an installed MDM from the official repo, you can save time and link the dependencies instead of getting them from scratch.

If you already have an installed MDM

Link from installed MDM

Before running the following bash script, first change the path to the full path to your installed MDM

bash prepare/link_mdm.sh

First time user

Download dependencies:

bash prepare/download_smpl_files.sh
bash prepare/download_glove.sh
bash prepare/download_t2m_evaluators.sh

Get HumanML3D dataset (For all applications):

Follow the instructions in HumanML3D, then copy the result dataset to our repository:

cp -r ../HumanML3D/HumanML3D ./dataset/HumanML3D

3. Get PriorMDM dependencies

DoubleTake (Long sequences)

BABEL dataset

Download the processed version here, and place it at ./dataset/babel

Download the following for evaluation here, and place it at ./dataset/babel

Download the following here, and place it at ./dataset/babel

SMPLH dependencies

Download here, and place it at ./body_models

ComMDM (two-person)

3DPW dataset

For ComMDM, we cleaned 3DPW and converted it to HumanML3D format.

Download the processed version here, and place it at ./dataset/3dpw

Fine-tuned motion control - No extra dependencies.

4. Download the pretrained models

Download the model(s) you wish to use, then unzip and place it in ./save/.

DoubleTake (long motions)

• my_humanml-encoder-512 (This is a reproduction of MDM best model without any changes)
• Babel_TrasnEmb_GeoLoss

ComMDM (two-person)

• pw3d_text (for text-to-motion)
• pw3d_prefix (for prefix completion)

Fine-tuned motion control

• root_horizontal_control (Finetuned the base model for 80,000 steps on (horizontal part of) root control objective)
• left_wrist_control (Finetuned the base model for 80,000 steps on left wrist control objective)
• right_foot_control (Finetuned the base model for 80,000 steps on right foor control objective)

Motion Synthesis

DoubleTake (long motions)

Reproduce random text prompts:

python -m sample.double_take --model_path ./save/my_humanml_trans_enc_512/model000200000.pt --num_samples 4 --handshake_size 20 --blend_len 10

Reproduce out of text file:

python -m sample.double_take --model_path ./save/my_humanml_trans_enc_512/model000200000.pt --handshake_size 20 --blend_len 10 --input_text ./assets/dt_text_example.txt 

Reproduce out of csv file (can determine each sequence length):

python -m sample.double_take --model_path ./save/my_humanml_trans_enc_512/model000200000.pt --handshake_size 20 --blend_len 10 --input_text ./assets/dt_csv_example.csv 

It will look something like this:

ComMDM (two-person)

Text-to-Motion

Reproduce paper text prompts:

python -m sample.two_person_text2motion --model_path ./save/pw3d_text/model000100000.pt --input_text ./assets/two_person_text_prompts.txt

It will look something like this:

Prefix completion

Complete unseen motion prefixes:

python -m sample.two_person_prefix_completion --model_path ./save/pw3d_prefix/model000050000.pt

It will look something like this:

Blue frames are the input prefix and orange frames are the generated completion.

Visualize dataset

Unfortunately, 3DPW dataset is not clean, even after our process. To get samples of it run:

python -m sample.two_person_text2motion --model_path ./save/humanml_trans_enc_512/model000200000.pt --sample_gt

Fine-tuned motion control

Horizontal Root Control

Sample the horizontal part of the root trajectory from the test set of HumanML3D, and generate a motion with the given trajectory (note that the vertical part of the trajectory is predicted by the model). To make the generation unconditioned on text we add --guidance_param 0.

python -m sample.finetuned_motion_control --model_path save/root_horizontal_finetuned/model000280000.pt --guidance_param 0

It will look something like this:

Use --show_input if you wish to plot the motion from which the control features were taken from.

Add a text condition with --text_condition. Note that by default, we use classifier-free-guidance with scale of 2.5.

python -m sample.finetuned_motion_control --model_path save/root_horizontal_finetuned/model000280000.pt --text_condition "a person is raising hands"

Left Wrist Control

Sample the relative trajectory of the left wrist w.r.t the root trajectory from the test set of HumanML3D, and generate a motion with the given left wrist relative trajectory. To make the generation unconditioned on text we add --guidance_param 0.

python -m sample.finetuned_motion_control --model_path save/left_wrist_finetuned/model000280000.pt --guidance_param 0

It will look something like this:

Add a text condition with --text_condition. Note that by default, we use classifier-free-guidance with scale of 2.5.

python -m sample.finetuned_motion_control --model_path save/left_wrist_finetuned/model000280000.pt --text_condition "a person is walking in a circle"

Left Wrist + Right Foot Control With Model Blending

Sample the relative trajectory of the left wrist w.r.t the root trajectory from the test set of HumanML3D, and generate a motion with the given left wrist relative trajectory. To make the generation unconditioned on text we add --guidance_param 0.

python -m sample.finetuned_motion_control --model_path save/left_wrist_finetuned/model000280000.pt,save/right_foot_finetuned/model000280000.pt --guidance_param 0

It will look something like this:

Add a text condition with --text_condition. Note that by default, we use classifier-free-guidance with scale of 2.5.

python -m sample.finetuned_motion_control --model_path save/left_wrist_finetuned/model000280000.pt,save/right_foot_finetuned/model000280000.pt --text_condition "a person is walking in a circle"

You may also define:

• --device id.
• --seed to sample different prompts.
• --motion_length (text-to-motion only) in seconds (maximum is 9.8[sec]).

Running those will get you:

• results.npy file with text prompts and xyz positions of the generated animation
• sample##_rep##.mp4 - a stick figure animation for each generated motion.

Render SMPL mesh

To create SMPL mesh per frame run:

python -m visualize.render_mesh --input_path /path/to/mp4/stick/figure/file

This script outputs:

• sample##_rep##_smpl_params.npy - SMPL parameters (thetas, root translations, vertices and faces)
• sample##_rep##_obj - Mesh per frame in .obj format.

Notes:

• The .obj can be integrated into Blender/Maya/3DS-MAX and rendered using them.
• This script is running SMPLify and needs GPU as well (can be specified with the --device flag).
• Important - Do not change the original .mp4 path before running the script.

Notes for 3d makers:

• You have two ways to animate the sequence:
  1. Use the SMPL add-on and the theta parameters saved to sample##_rep##_smpl_params.npy (we always use beta=0 and the gender-neutral model).
  2. A more straightforward way is using the mesh data itself. All meshes have the same topology (SMPL), so you just need to keyframe vertex locations. Since the OBJs are not preserving vertices order, we also save this data to the sample##_rep##_smpl_params.npy file for your convenience.

Train your own PriorMDM

DoubleTake (long motions)

HumanML3D best model Retraining HumanML3D is not needed as we use the original trained model from MDM. Yet, for completeness this repository supports this training as well:

python -m train.train_mdm --save_dir save/my_humanML_bestmodel --dataset humanml 

Babel best model

python -m train.train_mdm --save_dir ./save/my_Babel_TrasnEmb_GeoLoss --dataset babel --latent_dim 512 --batch_size 64 --diffusion_steps 1000 --num_steps 10000000 --min_seq_len 45 --max_seq_len 250 --lambda_rcxyz 1.0 --lambda_fc 1.0 --lambda_vel 1.0

ComMDM (two-person)

Text-to-Motion

Download the pretrained model for text-to-motion training from here and place it in ./save/. Then train with:

python -m train.train_mdm_multi --pretrained_path ./save/humanml_trans_enc_512/model000200000.pt --multi_train_mode text --multi_train_splits train,validation --save_dir ./save/my_pw3d_text

Prefix Completion

Download the pretrained model for prefix training from here and place it in ./save/. Then train with:

python -m train.train_mdm_multi --pretrained_path ./save/humanml_trans_enc_512_prefix_finetune/model000330000.pt --multi_train_mode prefix --save_dir ./save/my_pw3d_prefix --save_interval 10000

Finetuned Motion Control

Train a model for left wrist control from scratch on HumanML3D dataset.

python -m train.train_mdm_motion_control --save_dir save/left_wrist_finetuned --dataset humanml --inpainting_mask left_wrist

Finetune a base model for left wrist control on HumanML3D dataset. We advise setting --save_interval to 10,000 to have it saved more frequently, as this is a finetune and not training from scratch.

python -m train.train_mdm_motion_control --save_dir save/left_wrist_finetuned --dataset humanml --inpainting_mask left_wrist --resume_checkpoint save/humanml_trans_enc_512/model000200000.pt --save_interval 10_000

• Use --device to define GPU id.
• Add --train_platform_type {ClearmlPlatform, TensorboardPlatform} to track results with either ClearML or Tensorboard.
• Add --eval_during_training to run a short evaluation for each saved checkpoint. This will slow down training but will give you better monitoring.

Evaluate

DoubleTake (long motions)

To reproduce humanML3D evaluation over the motion run:

python -m eval.eval_humanml_double_take --model_path ./save/my_humanml_trans_enc_512/model000200000.pt --num_unfoldings 2 --handshake_size 20 --transition_margins 40  --eval_on motion --blend_len 10

To reproduce humanML3D evaluation over the transiton run:

python -m eval.eval_humanml_double_take --model_path ./save/my_humanml_trans_enc_512/model000200000.pt --num_unfoldings 2 --handshake_size 20 --transition_margins 40  --eval_on transition --blend_len 10

To reproduce BABEL evaluation over the motion run:

python -m eval.eval_multi --model_path ./save/Babel_TrasnEmb_GeoLoss/model001250000.pt --num_unfoldings 2 --cropping_sampler --handshake_size 30 --transition_margins 40  --eval_on motion --blend_len 10

To reproduce BABEL evaluation over the transiton run:

python -m eval.eval_multi --model_path ./save/Babel_TrasnEmb_GeoLoss/model001250000.pt --num_unfoldings 2 --cropping_sampler --handshake_size 30 --transition_margins 40  --eval_on transition --blend_len 10

ComMDM (two-person)

The reported evaluation for prefix completion is in ./save/pw3d_prefix/eval_prefix_pw3d_paper_results_000240000_wo_mm_1000samples.log.

To reproduce evaluation run:

python -m eval.eval_multi --model_path ./save/pw3d_prefix/model000240000.pt

Fine-tuned motion control

Evaluate the motion control models on the horizontal part of trajectories sampled from the test set of HumanML3D dataset.

python -m eval.eval_finetuned_motion_control --model_path save/root_horizontal_finetuned/model000280000.pt --replication_times 10

This code should produce a file named eval_humanml_root_horizontal_finetuned_000280000_gscale2.5_mask_root_horizontal_wo_mm.log, or generally: eval_humanml\_<model_name>\_gscale<guidance_free_scale>\_mask\_<name_of_control_features>_<evaluation_mode>.log

Acknowledgments

This code is standing on the shoulders of giants. We want to thank the following contributors that our code is based on:

MDM, guided-diffusion, MotionCLIP, text-to-motion, actor, joints2smpl, TEACH.

License

This code is distributed under an MIT LICENSE.

Note that our code depends on other libraries, including CLIP, SMPL, SMPL-X, PyTorch3D, and uses datasets that each have their own respective licenses that must also be followed.